Performance Efficiency
The Performance Efficiency pillar includes the ability to use computing resources efficiently to meet system requirements, and to maintain that efficiency as demand changes and technologies evolve.
The performance efficiency pillar provides an overview of design principles, best practices, and questions. You can find prescriptive guidance on implementation in the Performance Efficiency Pillar whitepaper.
Design Principles
There are five design principles for performance efficiency in the cloud:
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Democratize advanced technologies: Make advanced technology implementation easier for your team by delegating complex tasks to your cloud vendor. Rather than asking your IT team to learn about hosting and running a new technology, consider consuming the technology as a service. For example, NoSQL databases, media transcoding, and machine learning are all technologies that require specialized expertise. In the cloud, these technologies become services that your team can consume, allowing your team to focus on product development rather than resource provisioning and management.
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Go global in minutes: Deploying your workload in multiple AWS Regions around the world allows you to provide lower latency and a better experience for your customers at minimal cost.
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Use serverless architectures: Serverless architectures remove the need for you to run and maintain physical servers for traditional compute activities. For example, serverless storage services can act as static websites (removing the need for web servers) and event services can host code. This removes the operational burden of managing physical servers, and can lower transactional costs because managed services operate at cloud scale.
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Experiment more often: With virtual and automatable resources, you can quickly carry out comparative testing using different types of instances, storage, or configurations.
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Consider mechanical sympathy: Understand how cloud services are consumed and always use the technology approach that aligns best with your workload goals. For example, consider data access patterns when you select database or storage approaches.
Definition
There are four best practice areas for performance efficiency in the cloud:
Take a data-driven approach to building a high-performance architecture. Gather data on all aspects of the architecture, from the high-level design to the selection and configuration of resource types.
Reviewing your choices on a regular basis ensures that you are taking advantage of the continually evolving AWS Cloud. Monitoring ensures that you are aware of any deviance from expected performance. Make trade-offs in your architecture to improve performance, such as using compression or caching, or relaxing consistency requirements.
Best Practices
Selection
The optimal solution for a particular workload varies, and solutions often combine multiple approaches. Well-architected workloads use multiple solutions and enable different features to improve performance.
AWS resources are available in many types and configurations, which makes it easier to find an approach that closely matches your workload needs. You can also find options that are not easily achievable with on-premises infrastructure. For example, a managed service such as Amazon DynamoDB provides a fully managed NoSQL database with single-digit millisecond latency at any scale.
The following questions focus on these considerations for performance efficiency.
| PERF 1: How do you select the best performing architecture? |
Use a data-driven approach to select the patterns and implementation for your architecture and achieve a cost effective solution. AWS Solutions Architects, AWS Reference Architectures, and AWS Partner Network (APN) partners can help you select an architecture based on industry knowledge, but data obtained through benchmarking or load testing will be required to optimize your architecture.
Your architecture will likely combine a number of different architectural approaches (for example, event-driven, ETL, or pipeline). The implementation of your architecture will use the AWS services that are specific to the optimization of your architecture's performance. In the following sections we discuss the four main resource types to consider (compute, storage, database, and network).
Compute
Selecting compute resources that meet your requirements, performance needs, and provide great efficiency of cost and effort will enable you to accomplish more with the same number of resources. When evaluating compute options, be aware of your requirements for workload performance and cost requirements and use this to make informed decisions.
In AWS, compute is available in three forms: instances, containers, and functions:
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Instances are virtualized servers, allowing you to change their capabilities with a button or an API call. Because resource decisions in the cloud aren’t fixed, you can experiment with different server types. At AWS, these virtual server instances come in different families and sizes, and they offer a wide variety of capabilities, including solid-state drives (SSDs) and graphics processing units (GPUs).
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Containers are a method of operating system virtualization that allow you to run an application and its dependencies in resource-isolated processes. AWS Fargate is serverless compute for containers or Amazon EC2 can be used if you need control over the installation, configuration, and management of your compute environment. You can also choose from multiple container orchestration platforms: Amazon Elastic Container Service (ECS) or Amazon Elastic Kubernetes Service (EKS).
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Functions abstract the execution environment from the code you want to execute. For example, AWS Lambda allows you to execute code without running an instance.
The following questions focus on these considerations for performance efficiency.
| PERF 2: How do you select your compute solution? |
When architecting your use of compute you should take advantage of the elasticity mechanisms available to ensure you have sufficient capacity to sustain performance as demand changes.
Storage
Cloud storage is a critical component of cloud computing, holding the information used by your workload. Cloud storage is typically more reliable, scalable, and secure than traditional on-premises storage systems. Select from object, block, and file storage services as well as cloud data migration options for your workload.
In AWS, storage is available in three forms: object, block, and file:
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Object Storage provides a scalable, durable platform to make data accessible from any internet location for user-generated content, active archive, serverless computing, Big Data storage or backup and recovery. Amazon Simple Storage Service (Amazon S3) is an object storage service that offers industry-leading scalability, data availability, security, and performance. Amazon S3 is designed for 99.999999999% (11 9's) of durability, and stores data for millions of applications for companies all around the world.
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Block Storage provides highly available, consistent, low-latency block storage for each virtual host and is analogous to direct-attached storage (DAS) or a Storage Area Network (SAN). Amazon Elastic Block Store (Amazon EBS) is designed for workloads that require persistent storage accessible by EC2 instances that helps you tune applications with the right storage capacity, performance and cost.
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File Storage provides access to a shared file system across multiple systems. File storage solutions like Amazon Elastic File System (EFS) are ideal for use cases, such as large content repositories, development environments, media stores, or user home directories. Amazon FSx makes it easy and cost effective to launch and run popular file systems so you can leverage the rich feature sets and fast performance of widely used open source and commercially-licensed file systems.
The following questions focus on these considerations for performance efficiency.
| PERF 3: How do you select your storage solution? |
When you select a storage solution, ensuring that it aligns with your access patterns will be critical to achieving the performance you want.
Database
The cloud offers purpose-built database services that address different problems presented by your workload. You can choose from many purpose-built database engines including relational, key-value, document, in-memory, graph, time series, and ledger databases. By picking the best database to solve a specific problem (or a group of problems), you can break away from restrictive one-size-fits-all monolithic databases and focus on building applications to meet the performance needs of your customers.
In AWS you can choose from multiple purpose-built database engines including relational, key-value, document, in-memory, graph, time series, and ledger databases. With AWS databases, you don’t need to worry about database management tasks such as server provisioning, patching, setup, configuration, backups, or recovery. AWS continuously monitors your clusters to keep your workloads up and running with self-healing storage and automated scaling, so that you can focus on higher value application development.
The following questions focus on these considerations for performance efficiency.
| PERF 4: How do you select your database solution? |
Your workload's database approach has a significant impact on performance efficiency. It's often an area that is chosen according to organizational defaults rather than through a data-driven approach. As with storage, it is critical to consider the access patterns of your workload, and also to consider if other non-database solutions could solve the problem more efficiently (such as using graph, time series, or in-memory storage database).
Network
Since the network is between all workload components, it can have great impacts, both positive and negative, on workload performance and behavior. There are also workloads that are heavily dependent on network performance such as High Performance Computing (HPC) where deep network understanding is important to increase cluster performance. You must determine the workload requirements for bandwidth, latency, jitter, and throughput.
On AWS, networking is virtualized and is available in a number of different types and configurations. This makes it easier to match your networking methods with your needs. AWS offers product features (for example, Enhanced Networking, Amazon EBS-optimized instances, Amazon S3 transfer acceleration, and dynamic Amazon CloudFront) to optimize network traffic. AWS also offers networking features (for example, Amazon Route 53 latency routing, Amazon VPC endpoints, AWS Direct Connect, and AWS Global Accelerator) to reduce network distance or jitter.
The following questions focus on these considerations for performance efficiency.
| PERF 5: How do you configure your networking solution? |
You must consider location when deploying your network. You can choose to place resources close to where they will be used to reduce distance. Use networking metrics to make changes to networking configuration as the workload evolves. By taking advantage of Regions, placement groups, and edge services, you can significantly improve performance. Cloud based networks can be quickly re-built or modified, so evolving your network architecture over time is necessary to maintain performance efficiency.
Review
Cloud technologies are rapidly evolving and you must ensure that workload components are using the latest technologies and approaches to continually improve performance. You must continually evaluate and consider changes to your workload components to ensure you are meeting its performance and cost objectives. New technologies, such as machine learning and artificial intelligence (AI), can allow you to re-imagine customer experiences and innovate across all of your business workloads.
Take advantage of the continual innovation at AWS driven by customer need. We release new Regions, edge locations, services, and features regularly. Any of these releases could positively improve the performance efficiency of your architecture.
The following questions focus on these considerations for performance efficiency.
| PERF 6: How do you evolve your workload to take advantage of new releases? |
Architectures performing poorly are usually the result of a non-existent or broken performance review process. If your architecture is performing poorly, implementing a performance review process will allow you to apply Deming’s plan-do-check-act (PDCA) cycle to drive iterative improvement.
Monitoring
After you implement your workload, you must monitor its performance so that you can remediate any issues before they impact your customers. Monitoring metrics should be used to raise alarms when thresholds are breached.
Amazon CloudWatch is a monitoring and observability service that provides you with data and actionable insights to monitor your workload, respond to system-wide performance changes, optimize resource utilization, and get a unified view of operational health. CloudWatch collects monitoring and operational data in the form of logs, metrics, and events from workloads that run on AWS and on-premises servers. AWS X-Ray helps developers analyze and debug production, distributed applications. With AWS X-Ray, you can glean insights into how your application is performing and discover root causes and identify performance bottlenecks. You can use these insights to react quickly and keep your workload running smoothly.
The following questions focus on these considerations for performance efficiency.
| PERF 7: How do you monitor your resources to ensure they are performing? |
Ensuring that you do not see false positives is key to an effective monitoring solution. Automated triggers avoid human error and can reduce the time it takes to fix problems. Plan for game days, where simulations are conducted in the production environment, to test your alarm solution and ensure that it correctly recognizes issues.
Tradeoffs
When you architect solutions, think about tradeoffs to ensure an optimal approach. Depending on your situation, you could trade consistency, durability, and space for time or latency, to deliver higher performance.
Using AWS, you can go global in minutes and deploy resources in multiple locations across the globe to be closer to your end users. You can also dynamically add read-only replicas to information stores (such as database systems) to reduce the load on the primary database.
The following questions focus on these considerations for performance efficiency.
| PERF 8: How do you use tradeoffs to improve performance? |
As you make changes to the workload, collect and evaluate metrics to determine the impact of those changes. Measure the impacts to the system and to the end-user to understand how your trade-offs impact your workload. Use a systematic approach, such as load testing, to explore whether the tradeoff improves performance.
Resources
Refer to the following resources to learn more about our best practices for Performance Efficiency.
Performance Efficiency PillarAmazon S3 Performance Optimization
Amazon EBS Volume Performance
AWS re:Invent 2019: Amazon EC2 foundations (CMP211-R2)
AWS re:Invent 2019: Leadership session: Storage state of the union (STG201-L)
AWS re:Invent 2019: Leadership session: AWS purpose-built databases (DAT209-L)
AWS re:Invent 2019: Connectivity to AWS and hybrid AWS network architectures (NET317-R1)
AWS re:Invent 2019: Powering next-gen Amazon EC2: Deep dive into the Nitro system (CMP303-R2)
AWS re:Invent 2019: Scaling up to your first 10 million users (ARC211-R)